Colin J. Torney

Uninformed Individuals Promote Democratic Consensus in Animal Groups

Uninformed individuals inhibit extremism and enforce fair representation during collective decision-making. Conflicting interests among group members are common when making collective decisions, yet failure to achieve consensus can be costly. Under these circumstances individuals may be susceptible to manipulation by a strongly opinionated, or extremist, minority. It has previously been argued, for humans and animals, that social groups containing individuals who are uninformed, or exhibit weak preferences, are particularly vulnerable to such manipulative agents. Here, we use theory and experiment to demonstrate that, for a wide range of conditions, a strongly opinionated minority can dictate group choice, but the presence of uninformed individuals spontaneously inhibits this process, returning control to the numerical majority. Our results emphasize the role of uninformed individuals in achieving democratic consensus amid internal group conflict and informational constraints.

Emergent Sensing of Complex Environments by Mobile Animal Groups

The Power of the Collective Sensing the environment is generally considered to require significant cognitive sampling and comparison, not to mention time. However, species that do not necessarily have the cognitive ability, or the time, have also proven to be quite adept at sensing and evaluating their environment. Berdahl et al. (p. 574) show that in shiners, a species of schooling fish, mere attraction to, and movement toward, neighboring individuals allows the group to track preferred darkness in a variable-light environment. Living in a group amplifies the reach of an individuals capacity to sense environmental changes. The capacity for groups to exhibit collective intelligence is an often-cited advantage of group living. Previous studies have shown that social organisms frequently benefit from pooling imperfect individual estimates. However, in principle, collective intelligence may also emerge from interactions between individuals, rather than from the enhancement of personal estimates. Here, we reveal that this emergent problem solving is the predominant mechanism by which a mobile animal group responds to complex environmental gradients. Robust collective sensing arises at the group level from individuals modulating their speed in response to local, scalar, measurements of light and through social interaction with others. This distributed sensing requires only rudimentary cognition and thus could be widespread across biological taxa, in addition to being appropriate and cost-effective for robotic agents.

Context-dependent interaction leads to emergent search behavior in social aggregates

Locating the source of an advected chemical signal is a common challenge facing many living organisms. When the advecting medium is characterized by either high Reynolds number or high Peclet number, the task becomes highly nontrivial due to the generation of heterogeneous, dynamically changing filamental concentrations that do not decrease monotonically with distance to the source. Defining search strategies that are effective in these environments has important implications for the understanding of animal behavior and for the design of biologically inspired technology. Here we present a strategy that is able to solve this task without the higher intelligence required to assess spatial gradient direction, measure the diffusive properties of the flow field, or perform complex calculations. Instead, our method is based on the collective behavior of autonomous individuals following simple social interaction rules which are modified according to the local conditions they are experiencing. Through these context-dependent interactions, the group is able to locate the source of a chemical signal and in doing so displays an awareness of the environment not present at the individual level. This behavior illustrates an alternative pathway to the evolution of higher cognitive capacity via the emergent, group-level intelligence that can result from local interactions.

Specialization and evolutionary branching within migratory populations

Understanding the mechanisms that drive specialization and speciation within initially homogeneous populations is a fundamental challenge for evolutionary theory. It is an issue of relevance for significant open questions in biology concerning the generation and maintenance of biodiversity, the origins of reciprocal cooperation, and the efficient division of labor in social or colonial organisms. Several mathematical frameworks have been developed to address this question and models based on evolutionary game theory or the adaptive dynamics of phenotypic mutation have demonstrated the emergence of polymorphic, specialized populations. Here we focus on a ubiquitous biological phenomenon, migration. Individuals in our model may evolve the capacity to detect and follow an environmental cue that indicates a preferred migration route. The strategy space is defined by the level of investment in acquiring personal information about this route or the alternative tendency to follow the direction choice of others. The result is a relation between the migratory process and a game theoretic dynamic that is generally applicable to situations where information may be considered a public good. Through the use of an approximation of social interactions, we demonstrate the emergence of a stable, polymorphic population consisting of an uninformed subpopulation that is dependent upon a specialized group of leaders. The branching process is classified using the techniques of adaptive dynamics.

Signalling and the Evolution of Cooperative Foraging in Dynamic Environments

Understanding cooperation in animal social groups remains a significant challenge for evolutionary theory. Observed behaviours that benefit others but incur some cost appear incompatible with classical notions of natural selection; however, these behaviours may be explained by concepts such as inclusive fitness, reciprocity, intra-specific mutualism or manipulation. In this work, we examine a seemingly altruistic behaviour, the active recruitment of conspecifics to a food resource through signalling. Here collective, cooperative behaviour may provide highly nonlinear benefits to individuals, since group functionality has the potential to be far greater than the sum of the component parts, for example by enabling the effective tracking of a dynamic resource. We show that due to this effect, signalling to others is an evolutionarily stable strategy under certain environmental conditions, even when there is a cost associated to this behaviour. While exploitation is possible, in the limiting case of a sparse, ephemeral but locally abundant nutrient source, a given environmental profile will support a fixed number of signalling individuals. Through a quantitative analysis, this effective carrying capacity for cooperation is related to the characteristic length and time scales of the resource field.

The evolution of distributed sensing and collective computation in animal populations

Many animal groups exhibit rapid, coordinated collective motion. Yet, the evolutionary forces that cause such collective responses to evolve are poorly understood. Here, we develop analytical methods and evolutionary simulations based on experimental data from schooling fish. We use these methods to investigate how populations evolve within unpredictable, time-varying resource environments. We show that populations evolve toward a distinctive regime in behavioral phenotype space, where small responses of individuals to local environmental cues cause spontaneous changes in the collective state of groups. These changes resemble phase transitions in physical systems. Through these transitions, individuals evolve the emergent capacity to sense and respond to resource gradients (i.e. individuals perceive gradients via social interactions, rather than sensing gradients directly), and to allocate themselves among distinct, distant resource patches. Our results yield new insight into how natural selection, acting on selfish individuals, results in the highly effective collective responses evident in nature. DOI: http://dx.doi.org/10.7554/eLife.10955.001

Applications of machine learning in animal behaviour studies

In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them. These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods. The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data. In this review, we aim to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data. We start by describing the rationale behind ML and review a number of animal behaviour studies where ML has been successfully deployed. The ML framework is then introduced by presenting several unsupervised and supervised learning methods. Following this overview, we illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address. The first uses a large number of spectral and morphological characteristics that describe the appearance of pheasant, Phasianus colchicus, eggs to assign them to putative clutches. The second takes a continuous data stream of feeder visits from PIT (passive integrated transponder)-tagged jackdaws, Corvus monedula, and extracts foraging events from it, which permits the construction of social networks. Our final example uses aerial images to train a classifier that detects the presence of wildebeest, Connochaetes taurinus, to count individuals in a population. With the advent of cheaper sensing and tracking technologies an unprecedented amount of data on animal behaviour is becoming available. We believe that ML will play a central role in translating these data into scientific knowledge and become a useful addition to the animal behaviourists analytical toolkit.

Tactile interactions lead to coherent motion and enhanced chemotaxis of migrating cells

When motile cells come into contact with one another their motion is often considerably altered. In a process termed contact inhibition of locomotion (CIL) cells reshape and redirect their movement as a result of cell-cell contact. Here we describe a mathematical model that demonstrates that CIL alone is sufficient to produce coherent, collective cell migration. Our model illustrates a possible mechanism behind collective cell migration that is observed, for example, in neural crest cells during development, and in metastasizing cancer cells. We analyse the effects of varying cell density and shape on the alignment patterns produced and the transition to coherent motion. Finally, we demonstrate that this process may have important functional consequences by enhancing the accuracy and robustness of the chemotactic response, and factors such as cell shape and cell density are more significant determinants of migration accuracy than the individual capacity to detect environmental gradients.

The social context of cannibalism in migratory bands of the mormon cricket

Cannibalism has been shown to be important to the collective motion of mass migratory bands of insects, such as locusts and Mormon crickets. These mobile groups consist of millions of individuals and are highly destructive to vegetation. Individuals move in response to attacks from approaching conspecifics and bite those ahead, resulting in further movement and encounters with others. Despite the importance of cannibalism, the way in which individuals make attack decisions and how the social context affects these cannibalistic interactions is unknown. This can be understood by examining the decisions made by individuals in response to others. We performed a field investigation which shows that adult Mormon crickets were more likely to approach and attack a stationary cricket that was side-on to the flow than either head- or abdomen-on, suggesting that individuals could reduce their risk of an attack by aligning with neighbours. We found strong social effects on cannibalistic behaviour: encounters lasted longer, were more likely to result in an attack, and attacks were more likely to be successful if other individuals were present around a stationary individual. This local aggregation appears to be driven by positive feedback whereby the presence of individuals attracts others, which can lead to further crowding. This work improves our understanding of the local social dynamics driving migratory band formation, maintenance and movement at the population level.

On the evolutionary interplay between dispersal and local adaptation in heterogeneous environments

Dispersal, whether in the form of a dandelion seed drifting on the breeze, or a salmon migrating upstream to breed in a nonnatal stream, transports genes between locations. At these locations, local adaptation modifies the gene frequencies so their carriers are better suited to particular conditions, be those of newly disturbed soil or a quiet river pool. Both dispersal and local adaptation are major drivers of population structure; however, in general, their respective roles are not independent and the two may often be at odds with one another evolutionarily, each one exhibiting negative feedback on the evolution of the other. Here, we investigate their joint evolution within a simple, discrete‐time, metapopulation model. Depending on environmental conditions, their evolutionary interplay leads to either a monomorphic population of highly dispersing generalists or a collection of rarely dispersing, locally adapted, polymorphic sub‐populations, each adapted to a particular habitat type. A critical value of environmental heterogeneity divides these two selection regimes and the nature of the transition between them is determined by the level of kin competition. When kin competition is low, at the transition we observe discontinuities, bistability, and hysteresis in the evolved strategies; however, when high, kin competition moderates the evolutionary feedback and the transition is smooth.